first commit

.gitignore

@@ -0,0 +1,8 @@
+*.pth
+models*
+data/cache
+data/kitti/object
+*log.txt
+*.pyc
+*.so
+*.o

README.md

@@ -0,0 +1,116 @@
+# Stereo R-CNN
+## Stereo R-CNN based 3D Object Detection for Autonomous Driving
+
+<img src="doc/system.png" width = 90% div align=center />
+
+
+This project contains the implementation of our CVPR 2019 paper [arxiv](https://arxiv.org/pdf/1902.09738.pdf).
+
+Stereo R-CNN focuses on accurate 3D object detection and estimation using image-only data in autonomous driving scenarios. It features simultaneous object detection and association for stereo images, 3D box estimation using 2D information, accurate dense alignment for 3D box refinement. We also provide a light-weight version based on the monocular 2D detection, which only uses stereo images in the dense alignment module. Please checkout to branch mono for details.
+
+**Authors:** [Peiliang Li](https://peiliangli.github.io/), [Xiaozhi Chen](https://xiaozhichen.github.io/) and [Shaojie Shen](http://uav.ust.hk/group/) from the [HKUST Aerial Robotics Group](http://uav.ust.hk/), and [DJI](https://www.dji.com/).
+
+If you find the project useful for your research, please cite: 
+
+```
+@inproceedings{licvpr2019, 
+    title     = {Stereo R-CNN based 3D Object Detection for Autonomous Driving}, 
+    author    = {Li, Peiliang and Chen, Xiaozhi and Shen, Shaojie}, 
+    booktitle = {CVPR}, 
+    year      = {2019} 
+}
+```
+
+## 0. Install
+
+This implementation is tested under Pytorch 0.3.0. To avoid affecting your Pytorch version, we recommend using conda to enable multiple versions of Pytorch.
+
+0.0. Install Pytorch:
+```
+    conda create -n env_stereo python=2.7
+    conda activate env_stereo
+    conda install pytorch=0.3.0 cuda80 -c pytorch
+    conda install torchvision -c pytorch
+```
+0.1. Other dependencies:
+```
+    git clone [email protected]:PeiliangLi/Stereo-RCNN.git
+    cd stereo_rcnn
+    pip install -r requirements.txt
+```
+0.2. Build:
+```
+    cd lib
+    sh make.sh
+    cd ..
+```
+
+
+## 1. Quick Demo
+
+1.0. Set the folder for placing the model
+```
+    mkdir models_stereo
+```
+1.1. Download our trained weight [One Drive](https://hkustconnect-my.sharepoint.com/:u:/g/personal/pliap_connect_ust_hk/ESF-5mWMAK5PquLJQFs8evUB2xCbSwGuxbsUEeH9fcet6w?e=Vu0AzW)/[Google Drive](https://drive.google.com/file/d/1rZ5AsMms7-oO-VfoNTAmBFOr8O2L0-xt/view?usp=sharing) and put it into models_stereo/, then just run
+
+```
+    python demo.py
+```
+
+If everything goes well, you will see the detection result on the left, right and bird's eye view image respectively.
+
+## 2. Dataset Preparation
+
+2.0. Download the left image, right image, calibration, labels and point clouds (optional for visualization) from [KITTI Object Benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). Make sure the structure looks like:
+```
+yourfolder/object
+              training
+                image_2
+                image_3
+                label_2
+                calib
+                velodyne
+```
+2.1. Create symlinks:
+```
+    cd data/kitti
+    ln -s yourfolder/object object
+    cd ../..
+```
+## 3. Training
+
+Download the Res-101 pretrained weight [One Drive](https://hkustconnect-my.sharepoint.com/:u:/g/personal/pliap_connect_ust_hk/ESxYZL8TjzxAneYh_mWflFsBs0lZ-hc4m67W1k9_hYgXzQ?e=dZdoej)/[Google Drive](https://drive.google.com/file/d/1_t8TtUevtMdnvZ2SoD7Ut8sS1adyCKTt/view?usp=sharing), and put it into data/pretrained_model
+
+Set corresponding CUDA_VISIBLE_DEVICES in train.sh, and simply run
+```
+    ./train.sh
+```
+It consumes ~11G GPU memery. If your GPU memery is not enough, please try our light-weight version in branch mono.
+
+The trained model and training log are saved in /models_stereo by default.
+
+## 4. Evaluation
+
+You can evaluate the 3D detection performance using either our provided model or your trained model. 
+Set corresponding CUDA_VISIBLE_DEVICES in test.sh, and run
+```
+    ./test.sh
+```
+
+The results are saved in models_stereo/result by default. You can evaluate the results using the tool from [here](https://github.com/prclibo/kitti_eval).
+
+Some sample results:
+
+<img src="doc/results.png" width = 90% div align=center />
+
+## 5. Acknowledgments
+
+This repo is built based on the Faster RCNN implementation from [faster-rcnn.pytorch](https://github.com/jwyang/faster-rcnn.pytorch), and we also use the imagenet pretrained weight (originally provided from [here](https://filebox.ece.vt.edu/~jw2yang/faster-rcnn/pretrained-base-models/resnet101_caffe.pth)) for initialization.
+
+## 6. License
+The source code is released under [GPLv3](http://www.gnu.org/licenses/) license.
+
+We are still working on improving the code reliability. For any technical issues, please contact Peiliang Li <pliapATconnect.ust.hk>.
+
+For commercial inquiries, please contact Shaojie Shen <eeshaojieATust.hk>.

_init_paths.py

@@ -0,0 +1,12 @@
+import os.path as osp
+import sys
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+this_dir = osp.dirname(__file__)
+
+# Add lib to PYTHONPATH
+lib_path = osp.join(this_dir, 'lib')
+add_path(lib_path)